Hi Jarrod, I hadn't appreciated that the clustering of reproductive modes on the tree might limit out ability to detect some of these relationships. This is in fact a step in testing reproduction as an ordinal variable (egg-laying, lecithotrophic live-bearing, and matrotrophic live-bearing) which follows gradients of depth and latitude, and threshBayes cannot handle ordinal variables. Perhaps treating the data this way will help given more transitions. I have run the model in MCMCglmm and have appended the summary and attached the histogram of the liabilities. Thanks so much for your help with this...

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summary(dep2) Iterations = 3001:12991 Thinning interval = 10 Sample size = 1000 DIC: 31.2585 G-structure: ~animal post.mean l-95% CI u-95% CI eff.samp animal 82.18 35.88 140.1 6.266 R-structure: ~units post.mean l-95% CI u-95% CI eff.samp units 1 1 1 0 Location effects: parity ~ log.med.depth post.mean l-95% CI u-95% CI eff.samp pMCMC (Intercept) 0.4250 -13.5697 13.7913 28.54 0.946 log.med.depth -0.3601 -4.4399 3.8022 16.48 0.862 On Thu, Dec 15, 2016 at 11:10 PM, Jarrod Hadfield <j.hadfi...@ed.ac.uk> wrote: > Hi Chris, > > I think MCMCglmm is probably giving you the right answer. There are huge > chunks of the phylogeny that are either egg-laying and live-bearing. The > non-phylogenetic model shows a strong relationship between reproductive > mode and depth, and that might be causal or it might just be because > certain taxa tend to live at greater depths and *happen to have* the same > reproductive mode. There's not much information in the phylogenetic spread > of reproductive modes to distinguish between these hypotheses, hence the > wide confidence intervals. Just to be sure can you > > a) just perform independent contrasts (not really suitable for binary > data, but probably won't give you an answer far off the truth and is a nice > simple sanity check). > > b) using MCMCglmm (not MCMCglmmRAM) fit > > prior.dep2<-=list(R=list(V=diag(1), fix=1), G=list(G1=list(V=diag(1), > nu=0.002))) > > dep2<-MCMCglmm(parity~log.med.depth, > random=~animal, > rcov=~units, > pedigree=shark.tree, > data=traits, > prior=prior.dep2, > pr=TRUE, > pl=TRUE, > family="threshold") > > an send me the summary and hist(dep2$Liab) > > Cheers, > > Jarrod > > > > On 16/12/2016 07:02, Jarrod Hadfield wrote: > > Hi Chris, > > I think ngen in threshbayes is not the number of full iterations (i.e. a > full update of all parameters), but the number of full iterations > multiplied by the number of nodes (2n-1). With n=600 species this means > threshbayes has only really done about 8,000 iterations (i.e. about 1000X > less than MCMCglmm). Simulations suggest threshbayes is about half as > efficient per full iteration as MCMCglmm which means that it may have only > collected 0.5*1132/1200 = 0.47 effective samples from the posterior. The > very extreme value and the surprisingly tight credible intervals (+/-0.007) > also suggest a problem. > > **However**, the low effective sample size for the covariance in the > MCMglmm model is also worrying given the length of chain, and may point to > potential problems. Are egg-laying/live-bearing scattered throughout the > tree, or do they tend to aggregate a lot? Can you send me the output from: > > prior.dep<-=list(R=list(V=diag(1)*1e-15, fix=1), > G=list(G1=list(V=diag(1), fix=1))) > > dep<-MCMCglmm(parity~log.med.depth, > random=~animal, > rcov=~units, > pedigree=shark.tree, > reduced=TRUE, > data=traits, > prior=prior2, > pr=TRUE, > pl=TRUE, > family="threshold") > > summary(dep) > > summary(glm(parity~log.med.depth, data=traits, > family=binomial(link=probit))) > > Cheers, > > Jarrod > > > > On 15/12/2016 20:59, Chris Mull wrote: > > Hi All, > I am trying to look at the correlated evolution of traits using the > threshold model as implemented in phytools::threshBayes (Revell 2014) and > MCMCglmmRAM (Hadfield 2015). My understanding from Hadfield 2015 is that > the reduced animal models should yeild equivalent results, yet having run > both I am having trouble reconciling the results. I have a tree covering > ~600 species of sharks. skates, and rays and am interested in testing for > the correlated evolution between reproductive mode (egg-laying and > live-bearing) with depth. When I test for this correlation using > phytools:threshBayes there is a clear result with a high correlation > coefficient (-0.986) as I would predict. When I run the same analysis using > MCMCglmmRAM I get a very different result, with a low degree of covariation > and CI crossing zero (-0.374; -3.638 - 3.163 95%CI). Both models are run > for 10,000,000 generations with the same bunr-in and sampling period. I > have looked at the trace plots for each model using coda and parameter > estimates and likelihodd . I'm not sure how to reconcile the differences in > these results and any advice would be greatly appreciated. I have appended > the code and outputs below. > > > ####################### > #phytools::threshBayes# > ####################### > > X<-shark.data[c("parity","log.med.depth")] > X<-as.matrix(X) > > #mcmc paramters (also see control options) > ngen<-10000000 > burnin<-0.2*ngen > sample<-500 > > thresh<-threshBayes(shark.tree, > X, > types=c("discrete","continuous"), > ngen=ngen, > control = list(sample=sample)) > > The return correlation is -0.986 (-0.99 - -0.976 95%HPD) > > > ############################# > #MCMCglmm bivariate-gaussian# > ############################# > > > prior2=list(R=list(V=diag(2)*1e-15, fix=1), G=list(G1=list(V=diag(2), > > nu=0.002, fix=2))) > > ellb.log.dep<-MCMCglmm(cbind(log.med.depth,parity)~trait-1, > random=~us(trait):animal, > rcov=~us(trait):units, > pedigree=shark.tree, > reduced=TRUE, > data=traits, > prior=prior2, > pr=TRUE, > pl=TRUE, > family=c("gaussian", "threshold"), > thin=500, > burnin = 1000000, > nitt = 10000000) > > summary(ellb.log.dep) > > Iterations = 1000001:9999501 > Thinning interval = 500 > Sample size = 18000 > DIC: 2930.751 > G-structure: ~us(trait):animal > post.mean l-95% CI u-95% CI eff.samp > traitscale.depth:traitscale.depth.animal 16.965 15.092 18.864 > 18000 > traitparity:traitscale.depth.animal -0.374 -3.638 3.163 > 1132 > traitscale.depth:traitparity.animal -0.374 -3.638 3.163 > 1132 > traitparity:traitparity.animal 1.000 1.000 1.000 > 0 > R-structure: ~us(trait):units > post.mean l-95% CI u-95% CI eff.samp > traitscale.depth:traitscale.depth.units 16.965 15.092 18.864 18000 > traitparity:traitscale.depth.units -0.374 -3.638 3.163 1132 > traitscale.depth:traitparity.units -0.374 -3.638 3.163 1132 > traitparity:traitparity.units 1.000 1.000 1.000 0 > Location effects: cbind(scale.depth, parity) ~ trait - 1 > post.mean l-95% CI u-95% CI eff.samp pMCMC > traitscale.depth 0.12297 -3.63655 4.02005 18000 0.949 > traitparity -0.02212 -1.00727 0.93387 17058 0.971 > > Thanks for any and all input. > > Cheers, > Chris > > > > > > The University of Edinburgh is a charitable body, registered in > Scotland, with registration number SC005336. > > -- Christopher Mull PhD Candidate, Shark Biology and Evolutionary Neuroecology Dulvy Lab Simon Fraser University Burnaby,B.C. V5A 1S6 Canada (778) 782-3989 twitter: @mrsharkbrain e-mail:cm...@sfu.ca www.christophermull.weebly.com www.earth2ocean.org [[alternative HTML version deleted]] _______________________________________________ R-sig-phylo mailing list - R-sig-phylo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-phylo Searchable archive at http://www.mail-archive.com/r-sig-phylo@r-project.org/